Sr. Manager, Software Engineering - Machine Learning Simulations

Role overview

Lead the engineering team behind a marketplace simulation platform that replays historical data through production pricing logic in isolated environments. The platform answers a core question before any change reaches a borrower: what would a new ML model, price, or fee have produced for real applicants? Currently used by machine learning teams for roughly a thousand simulations per month, the platform needs to mature into production-grade infrastructure that is more reliable, lower-cost, and accessible to analytics and finance partners.

Responsibilities

- Own the engineering roadmap for the simulation platform, prioritizing reliability, fidelity to production, cost efficiency, and broader coverage across the lending funnel

- Lead, coach, and grow a team of engineers working across distributed services, offline data pipelines, and large-scale compute

- Raise simulation reliability and accuracy to a level that teams trust for launch decisions, including automated checks that detect drift between simulated and production results

- Reduce per-simulation cost as usage grows, through smart architecture and compute choices

- Expand platform coverage across the lending funnel and into new loan products, partnering with teams that own each stage of pricing and decisioning logic

- Turn recurring questions from machine learning, analytics, and capital markets partners into self-serve simulation workflows

Requirements

- Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field, or equivalent practical experience

- 8+ years of software engineering experience, including 3+ years of engineering management experience

- Experience leading teams that build and operate backend platforms or distributed systems in production

- Experience delivering platform or infrastructure programs that span multiple teams

- Proficiency with Python, Kotlin, AWS, Kubernetes, and Databricks or similar data tooling

- Knowledge of simulation, backtesting, experimentation, or offline model evaluation systems

- Experience supporting ML teams or operating MLOps infrastructure, including model inference, workflow orchestration, and offline feature pipelines

Nice to have

- Knowledge of lending, pricing, credit risk, or marketplace economics

- Ability to treat compute cost and efficiency as an engineering goal alongside reliability

- Experience building shared platforms where partner teams own part of the logic, and keeping those interfaces steady as systems evolve

- Experience developing senior engineers and emerging engineering leaders

Benefits and work setup

- Anticipated base salary range of $195,300–$270,400 USD

- Competitive compensation including base pay, bonus opportunities, and annual equity grants that vest quarterly

- Retirement benefits with company match up to a defined annual cap

- Employee Stock Purchase Plan with discounted stock purchase options

- Comprehensive medical, dental, vision, and wellness coverage, plus Health Savings Account contributions where eligible

- Life insurance, disability coverage, paid time off, sick leave, and company holidays

- Paid family and parental leave, family-centered benefits, and an Employee Assistance Program for mental health support

- Financial wellness resources, annual wellness allowance, and annual productivity allowance

- Team events, all-company updates, and employee resource groups

- Remote-first with most employees living and working anywhere in the US, paired with regular in-person team onsites roughly once or twice per quarter for 2–4 consecutive days

- Team operates on East and West coast time zones